Financial Analyst Skill

What this skill covers

This page keeps a stable Remote OpenClaw URL for the upstream skillwhile preserving the original source content below. The shell stays consistent, and the body can vary as much as the upstream SKILL.md or README varies.

Source files and registry paths

# Financial Analyst Skill

## Overview

Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.

## 5-Phase Workflow

### Phase 1: Scoping
- Define analysis objectives and stakeholder requirements
- Identify data sources and time periods
- Establish materiality thresholds and accuracy targets
- Select appropriate analytical frameworks

### Phase 2: Data Analysis & Modeling
- Collect and validate financial data (income statement, balance sheet, cash flow)
- **Validate input data completeness** before running ratio calculations (check for missing fields, nulls, or implausible values)
- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
- Build DCF models with WACC and terminal value calculations; **cross-check DCF outputs against sanity bounds** (e.g., implied multiples vs. comparables)
- Construct budget variance analyses with favorable/unfavorable classification
- Develop driver-based forecasts with scenario modeling

### Phase 3: Insight Generation
- Interpret ratio trends and benchmark against industry standards
- Identify material variances and root causes
- Assess valuation ranges through sensitivity analysis
- Evaluate forecast scenarios (base/bull/bear) for decision support

### Phase 4: Reporting
- Generate executive summaries with key findings
- Produce detailed variance reports by department and category
- Deliver DCF valuation reports with sensitivity tables
- Present rolling forecasts with trend analysis

### Phase 5: Follow-up
- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
- Monitor report delivery timeliness (target: 100% on time)
- Update models with actuals as they become available
- Refine assumptions based on variance analysis

## Tools

### 1. Ratio Calculator (`scripts/ratio_calculator.py`)

Calculate and interpret financial ratios from financial statement data.

**Ratio Categories:**
- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin
- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio
- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR
- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO
- **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio

```bash
python scripts/ratio_calculator.py sample_financial_data.json
python scripts/ratio_calculator.py sample_financial_data.json --format json
python scripts/ratio_calculator.py sample_financial_data.json --category profitability
```

### 2. DCF Valuation (`scripts/dcf_valuation.py`)

Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.

**Features:**
- WACC calculation via CAPM
- Revenue and free cash flow projections (5-year default)
- Terminal value via perpetuity growth and exit multiple methods
- Enterprise value and equity value derivation
- Two-way sensitivity analysis (discount rate vs growth rate)

```bash
python scripts/dcf_valuation.py valuation_data.json
python scripts/dcf_valuation.py valuation_data.json --format json
python scripts/dcf_valuation.py valuation_data.json --projection-years 7
```

### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)

Analyze actual vs budget vs prior year performance with materiality filtering.

**Features:**
- Dollar and percentage variance calculation
- Materiality threshold filtering (default: 10% or $50K)
- Favorable/unfavorable classification with revenue/expense logic
- Department and category breakdown
- Executive summary generation

```bash
python scripts/budget_variance_analyzer.py budget_data.json
python scripts/budget_variance_analyzer.py budget_data.json --format json
python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000
```

### 4. Forecast Builder (`scripts/forecast_builder.py`)

Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.

**Features:**
- Driver-based revenue forecast model
- 13-week rolling cash flow projection
- Scenario modeling (base/bull/bear cases)
- Trend analysis using simple linear regression (standard library)

```bash
python scripts/forecast_builder.py forecast_data.json
python scripts/forecast_builder.py forecast_data.json --format json
python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear
```

## Knowledge Bases

| Reference | Purpose |
|-----------|---------|
| `references/financial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |
| `references/valuation-methodology.md` | DCF methodology, WACC, terminal value, comps |
| `references/forecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |
| `references/industry-adaptations.md` | Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |

## Templates

| Template | Purpose |
|----------|---------|
| `assets/variance_report_template.md` | Budget variance report template |
| `assets/dcf_analysis_template.md` | DCF valuation analysis template |
| `assets/forecast_report_template.md` | Revenue forecast report template |

## Key Metrics & Targets

| Metric | Target |
|--------|--------|
| Forecast accuracy (revenue) | +/-5% |
| Forecast accuracy (expenses) | +/-3% |
| Report delivery | 100% on time |
| Model documentation | Complete for all assumptions |
| Variance explanation | 100% of material variances |

## Input Data Format

All scripts accept JSON input files. See `assets/sample_financial_data.json` for the complete input schema covering all four tools.

## Dependencies

**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.

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